Learning to Consider Relevance and Redundancy Dynamically for Abstractive Multi-document Summarization

Liu, Yiding; Fan, Xiaoning; Zhou, Jie; He, Chenglong; Liu, Gongshen

doi:10.1007/978-3-030-60450-9_38

Cited by 3 publications

(1 citation statement)

References 15 publications

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“…Abstractive Document Summarization. Neural models for abstractive document summarization have been widely studied [25,26] since Rush et al [27] first employed the encoder and decoder framework. A series of improvements have been proposed based on different advanced techniques.…”

Section: Related Workmentioning

confidence: 99%

WHORU: Improving Abstractive Dialogue Summarization with Personal Pronoun Resolution

Zhou

2023

Electronics

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With the abundance of conversations happening everywhere, dialogue summarization plays an increasingly important role in the real world. However, dialogues inevitably involve many personal pronouns, which hinder the performance of existing dialogue summarization models. This work proposes a framework named WHORU to inject external personal pronoun resolution (PPR) information into abstractive dialogue summarization models. A simple and effective PPR method for the dialogue domain is further proposed to reduce time and space consumption. Experiments demonstrated the superiority of the proposed methods. More importantly, WHORU achieves new SOTA results on SAMSum and AMI datasets.

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Section: Related Workmentioning

confidence: 99%

WHORU: Improving Abstractive Dialogue Summarization with Personal Pronoun Resolution

Zhou

2023

Electronics

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“Sankshepan”—Summarizing Kannada Text Using BART Transformer

Devendra Venkat Nadh,

Rakesh Reddy,

Teja Reddy

et al. 2024

Data-Intensive Research

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WHORU: Improving Abstractive Dialogue Summarization with Personal Pronoun Resolution

Zhou¹

2023

Preprint

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With the abundance of conversations happening everywhere, dialogue summarization plays an increasingly important role in the real world. However, dialogues inevitably involve lots of personal pronouns, which hinder the performance of existing dialogue summarization models. This work proposes a framework named WHORU to inject external personal pronoun resolution (PPR) information into abstractive dialogue summarization models. To reduce time and space consumption, we further propose a simple and effective PPR method for the dialogue domain. Experiments demonstrated the superiority of the proposed methods. More importantly, we achieve new SOTA results on SAMSum and AMI datasets.

show abstract

Learning to Consider Relevance and Redundancy Dynamically for Abstractive Multi-document Summarization

Cited by 3 publications

References 15 publications

WHORU: Improving Abstractive Dialogue Summarization with Personal Pronoun Resolution

WHORU: Improving Abstractive Dialogue Summarization with Personal Pronoun Resolution

“Sankshepan”—Summarizing Kannada Text Using BART Transformer

WHORU: Improving Abstractive Dialogue Summarization with Personal Pronoun Resolution

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